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Record W4412805137 · doi:10.1016/j.cnsns.2025.109158

Hypergeometric behavior of metal oxide varistors in DC circuit breakers

2025· article· en· W4412805137 on OpenAlexaff
Yang Liu, Zhi Jin Zhang, Kayla Chuong, Zhiyang Jin, Alfonso Cruz, Lauren M. Garten, Lukas Graber

Bibliographic record

VenueCommunications in Nonlinear Science and Numerical Simulation · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicVacuum and Plasma Arcs
Canadian institutionsUniversity of British Columbia
FundersAdvanced Research Projects Agency - Energy
KeywordsVaristorCircuit breakerMaterials scienceOxideMetalElectrical engineeringComposite materialMetallurgyEngineeringVoltage

Abstract

fetched live from OpenAlex

This paper analyzes the formula of current waveforms of metal oxide varistors (MOVs) in dc circuit breaker (DCCB) applications. Firstly, the nonlinear integral equation of a DCCB circuit is solved. DCCB operational metrics derivatives from the solution are taken with respect to MOV nonlinearity coefficients to attain their polarities. Both the solution and the polarities are experimentally validated through DCCB MOV accelerated degradation tests. The solution involves Gauss hypergeometric function, demonstrating a hypergeometric behavior of DCCB MOVs. Polarities of the derivatives also suggest increased nonlinearity coefficients for reliable DCCB design due to reduced MOV energy, charge, and conduction time. The discovery of DCCB MOV hypergeometric behavior and polarities of its metrics illustrates the strength of the hypergeometric model. Besides, multi-disciplinary applications of the model are found, and an asymptotic approximation of the model by the conventional linear model is established. • The first to present the exact solution to the nonlinear integral equation of a DCCB. • The solution exhibits hypergeometric nature, can be used as a better (new) model. • The new model can relate DCCB behavior metrics with MOV nonlinear coefficients. • The new model facilitates DCCB MOV reliability study and can improve DCCB design. • The new model also applies in other systems with the same nonlinear analogy circuit. • Asymptotic approximations and nested limits are found for the hypergeometric model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.331
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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